Datasets:
File size: 1,685 Bytes
04282b5 271c31b 04282b5 50abc50 04282b5 50abc50 04282b5 271c31b 04282b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
---
license: apache-2.0
task_categories:
- text-classification
tags:
- protein
- downstream task
---
# DeepLocMulti Dataset with ESMFold Structural Sequence
- Description: Protein localization encompasses the processes that establish and maintain proteins at specific locations.
- Number of labels: 10
- Problem Type: single_label_classification
- Columns:
- aa_seq: protein amino acid sequence
- foldseek_seq: foldseek 20 3di structural sequence
- ss8_seq: DSSP 8 secondary structure sequence
- location: Specific location
# Github
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
https://github.com/tyang816/SES-Adapter
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning
https://github.com/ai4protein/VenusFactory
# Citation
Please cite our work if you use our dataset.
```
@article{tan2024ses-adapter,
title={Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models},
author={Tan, Yang and Li, Mingchen and Zhou, Bingxin and Zhong, Bozitao and Zheng, Lirong and Tan, Pan and Zhou, Ziyi and Yu, Huiqun and Fan, Guisheng and Hong, Liang},
journal={Journal of Chemical Information and Modeling},
year={2024},
publisher={ACS Publications}
}
@article{tan2025venusfactory,
title={VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning},
author={Tan, Yang and Liu, Chen and Gao, Jingyuan and Wu, Banghao and Li, Mingchen and Wang, Ruilin and Zhang, Lingrong and Yu, Huiqun and Fan, Guisheng and Hong, Liang and Zhou, Bingxin},
journal={arXiv preprint arXiv:2503.15438},
year={2025}
}
``` |